Using a large number of parameters for modeling and training results in overfitting and exponentially growing computational cost.
It also renders the networks to being more data-driven which makes them unable to work under a small change in the point set.
Although several works [1]}, [2]}, [3]} show less tendency towards overfitting, their performance is highly dependent on the samples.
Most models are susceptible to irregular samples and work only under certain conditions.
Meanwhile, developing highly accurate, robust and fast models for the processing of 3D data is demanded by many practical applications like autonomous driving.
